Safetensors
MLX
English
mlx-lm
minimax_m2
quantization
mixed_3_6
minimax
custom_code
4-bit precision
Instructions to use petergilani/MiniMax-M2.5-mix3-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use petergilani/MiniMax-M2.5-mix3-6bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir MiniMax-M2.5-mix3-6bit petergilani/MiniMax-M2.5-mix3-6bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
base_model: MiniMaxAI/MiniMax-M2.5 language: en library_name: mlx-lm license: modified-mit model_name: MiniMax-M2.5-mix3-6bit tags: - quantization - mixed_3_6 - minimax - mlx
MiniMax-M2.5-mix3-6bit
Mixed precision quantized version of MiniMax M2.5 using mlx-lm with --quant-predicate mixed_3_6.
Model Details
| Property | Value |
|---|---|
| Base Model | MiniMaxAI/MiniMax-M2.5 |
| Quantization | mlx-lm v0.30.7 with --quant-predicate mixed_3_6 |
| Library | mlx-lm |
| License | modified-mit |
Inference Parameters
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_p | 0.95 |
| top_k | 40 |
Usage
import mlx_lm
from mlx_lm.sample_utils import make_sampler
model_path = "petergilani/MiniMax-M2.5-mix3-6bit"
model, tokenizer = mlx_lm.load(model_path)
sampler = make_sampler(temp=1.0, top_p=0.95, top_k=40)
prompt = "Your prompt here"
response = mlx_lm.generate(
model,
tokenizer,
prompt=prompt,
sampler=sampler,
max_tokens=512
)
print(response)
- Downloads last month
- 42
Model size
229B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
Log In to add your hardware
4-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for petergilani/MiniMax-M2.5-mix3-6bit
Base model
MiniMaxAI/MiniMax-M2.5